Detection of Cervical Lesion Cell/Clumps Based on Adaptive Feature Extraction

• Published in: Bioengineering (Basel) Vol. 11; no. 7; p. 686
• Main Authors: Li, Gang, Li, Xingguang, Wang, Yuting, Gong, Shu, Yang, Yanting, Xu, Chuanyun
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 05.07.2024
• Subjects: Accuracy; adaptive feature extraction; Artificial intelligence; Bias; bounding box loss; Boxes; Cell size; Cervical cancer; Classification; Clumps; Deep learning; Feature extraction; Human papillomavirus; Immunization; Lesions; Methods; Neural networks; Parameters; Target detection; target detection; bounding box loss; cervical cancer; adaptive feature extraction
• ISSN: 2306-5354; 2306-5354
• DOI: 10.3390/bioengineering11070686

Automated detection of cervical lesion cell/clumps in cervical cytological images is essential for computer-aided diagnosis. In this task, the shape and size of the lesion cell/clumps appeared to vary considerably, reducing the detection performance of cervical lesion cell/clumps. To address the issue, we propose an adaptive feature extraction network for cervical lesion cell/clumps detection, called AFE-Net. Specifically, we propose the adaptive module to acquire the features of cervical lesion cell/clumps, while introducing the global bias mechanism to acquire the global average information, aiming at combining the adaptive features with the global information to improve the representation of the target features in the model, and thus enhance the detection performance of the model. Furthermore, we analyze the results of the popular bounding box loss on the model and propose the new bounding box loss tendency-IoU (TIoU). Finally, the network achieves the mean Average Precision (mAP) of 64.8% on the CDetector dataset, with 30.7 million parameters. Compared with YOLOv7 of 62.6% and 34.8M, the model improved mAP by 2.2% and reduced the number of parameters by 11.8%.